Solving a Class of Stochastic Minimization Problems

Michael P. Bailey
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Michael P. Bailey: Naval Postgraduate School, Monterey, California

Operations Research, 1994, vol. 42, issue 3, 428-438

Abstract: This work gives a methodology for analyzing a class of discrete minimization problems with random element weights. The minimum weight solution is shown to be an absorbing state in a Markov chain, while the distribution of weight of the minimum weight element is shown to be of phase type. We then present two-sided bounds for matroids with NBUE distributed weights, as well as for weights with bounded positive hazard rates. We illustrate our method using a realistic military communications problem.

Keywords: mathematics: combinatorics; networks/graphs: stochastic; probability: Markov processes (search for similar items in EconPapers)
Date: 1994
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:42:y:1994:i:3:p:428-438

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